Download Applications of data mining in health and pharmaceutical

International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013
ISSN 2229-5518
915
Applications of data mining in health and
pharmaceutical industry
Sandhya Joshi, Hanumanthachar Joshi
Abstract— The Data mining is gradually becoming an integral and essential part of health and pharmaceutical industry. Data mining techniques
are being regularly used to assess efficacy of treatment, management of ailments, and also in various stages of drug discovery and process research. Off late, data mining has become a boon in health insurance sector to minimize frauds and abuse. A good number of health care companies, medical hospitals and pharmaceutical manufacturing units are employing the data mining tools due their excellent efficiency. The identification and quantification of pharmaceutical information can be extremely useful for patients, physicians, pharmacists, health organizations, insurance companies, regulatory agencies, investors, lawyers, pharmaceutical manufacturers, drug testing companies. This review deals with the major applications of data mining techniques in health care and pharmaceutical industries with relevant examples.
Index Terms—. Data mining, health care, pharmaceutical industry, classification, insurance, drug, Alzheimer’s
——————————  ——————————
1 INTRODUCTION
D
ata mining is a fast evolving technology, is being adopted in biomedical sciences and research. Modern medicine
generates a great deal of information stored in the medical
database. For example, medical data may contain MRIs, signals like ECG, clinical information like blood sugar, blood
pressure, cholesterol levels, etc., as well as the physician's interpretation. Extracting useful knowledge and providing scientific decision-making for the diagnosis and treatment of disease from the database increasingly becomes necessary [1].
Data mining in medicine can deal with this problem. It can
also improve the management level of hospital information
and promote the development of telemedicine and community
medicine [2]. The goal of data mining in clinical medicine is to
derive models that can use patient specific information to predict the outcome of interest and to thereby support clinical
decision-making.
In healthcare, data mining is becoming increasingly popular. Several factors have motivated the use of data mining applications in healthcare. The existence of medical insurance
fraud and abuse, for example, has led many healthcare insurers to attempt to reduce their losses by using data mining tools
to help them find and track offenders [3]. Fraud detection using data mining applications is prevalent in the commercial
world, for example, in the detection of fraudulent credit card
transactions.
————————————————
 Associate Professor, Pooja Bhagavat Memorial Mahajana post graduate
center, Mysore, India.PH- 09480126436 E-mail: [email protected]
 Professor, Sarada Vilas College of Pharmacy, K.M. Puram, Mysore. PH09663373701. E-mail: [email protected]
Recently, there have been reports of successful data mining
applications in healthcare fraud and abuse detection. Another
factor is that the huge amounts of data generated by
healthcare transactions are too complex and voluminous to be
processed and analyzed by traditional methods. Data mining
can improve decision-making by discovering patterns and
trends in large amounts of complex data [4]. Such analysis has
become increasingly essential as financial pressures have
heightened the need for healthcare organizations to make decisions based on the analysis of clinical and financial data. Insights gained from data mining can influence cost, revenue,
and operating efficiency while maintaining a high level of care
[5].
Healthcare organizations that perform data mining are better
positioned to meet their long-term needs [6]. Data can be a
great asset to healthcare organizations, but they have to be
first transformed into information. Yet another factor motivating the use of data mining applications in healthcare is the
realization that data mining can generate information that is
very useful to all parties involved in the healthcare industry.
For example, data mining applications can help healthcare
insurers detect fraud and abuse, and healthcare providers can
gain assistance in making decisions, for example, in customer
relationship management. Data mining applications also can
benefit healthcare providers, such as hospitals, clinics and
physicians, and patients, for example, by identifying effective
treatments and best practices [7,8] The Centers for Medicare
and Medicaid Services has used data mining to develop a prospective payment system for inpatient rehabilitation [9] The
healthcare industry can benefit greatly from data mining applications. This review is intended to explore relevant data
mining applications, understand methodologies involved and
their potentials in healthcare and pharmaceutical industry.
IJSER © 2013
http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013
ISSN 2229-5518
The term data mining, in its most common use, is very new.
The term previously had been used pejoratively by some statisticians and other specialists to refer to the process of analyzing the same data repeatedly until an acceptable result arose
[10]. By the early 1990s, a number of forces converged to make
data mining a very hot topic. It has subsequently been widely
applied in retailing, banking and financial services, insurance,
marketing and sales and telecommunications.
At first, parts of the scientific community were slow to embrace data mining. This was at least partially due to the marketing hype and wild claims made by some software salespeople and consultants. However, data mining is now moving
into the mainstream of science and engineering. Data mining
has come of age because of the confluence of three factors. The
first is the ability to inexpensively capture, store and process
tremendous amounts of data. The second is advances in database technology that allow the stored data to be organised and
stored in ways that facilitate speedy answers to complex queries. Finally, there are developments and improvements in
analysis methods that allow them to be effectively applied to
these very large and complex databases. It is important to remember that data mining is a tool, not a magic wand. You
can’t simply throw your data at a data mining tool and expect
it to produce reliable or even valid results. You still need to
know your business, to understand your data, and to understand the analytical methods you use. Furthermore, the patterns uncovered by data mining must be verified in the real
world. Just because data mining predicts that a gene will express a particular protein, or that a drug is best sold to a certain group of physicians, it doesn’t mean this prediction is valid in the real world. You still need to verify the prediction with
experiments to confirm the existence of a causal relationship.
Healthcare Data Mining Applications include evaluation of
treatment effectiveness; management of healthcare; customer
relationship management; and detection of fraud and abuse.
Effectiveness of Treatment: Data mining applications can be
developed to evaluate the effectiveness of medical treatments.
By comparing the causes, symptoms, and courses of treatments, data mining can deliver an analysis of which courses of
action prove effective [11]. For example, the outcomes of patient groups treated with different drug regimens for the same
disease or condition can be compared to determine which
treatments work best and are most cost-effective [12]. Along
this line, United HealthCare has mined its treatment record
data to explore ways to cut costs and deliver better medicine
[13]. It also has developed clinical profiles to give physicians
information about their practice patterns and to compare these
with those of other physicians and peer-reviewed industry
standards.
916
Similarly, data mining can help identify successful standardized treatments for specific diseases. In 1999, Florida Hospital
launched the clinical best practices initiative with the goal of
developing a standard path of care across all campuses, clinicians, and patient admissions.8 Florida Hospital used data
mining in its daily activities [7,14]. Other data mining applications are associating the various side-effects of treatment, collating common symptoms to aid diagnosis, determining the
most effective drug compounds for treating sub-populations
that respond differently from the mainstream population to
certain drugs, and determining proactive steps that can reduce
the risk of affliction [12].
Group Health Cooperative stratifies its patient populations
by demographic characteristics and medical conditions to determine which groups use the most resources, enabling it to
develop programs to help educate these populations and prevent or manage their conditions [11]. Group Health Cooperative has been involved in several data mining efforts to give
better healthcare at lower costs. In the Seton Medical Center,
data mining is used to decrease patient length-of-stay, avoid
clinical complications, develop best practices, improve patient
outcomes, and provide information to physicians—all to
maintain and improve the quality of healthcare [15].
For example, Blue Cross has been implementing data mining
initiatives to improve outcomes and reduce expenditures
through better disease management. For instance, it uses
emergency department and hospitalization claims data,
pharmaceutical records, and physician interviews to identify
unknown asthmatics and develop appropriate interventions
[11]. Data mining also can be used to identify and understand
high-cost patients.5 At a Data mining can also facilitate comparisons across healthcare groups of things such as practice
patterns, resource utilization, length of stay, and costs of different hospitals [16]. Recently, Sierra Health Services has used
data mining extensively to identify areas for quality improvements, including treatment guidelines, disease management
groups, and cost management [17].
Customer Potential Management Corp. has developed a
Consumer Healthcare Utilization Index that provides an indication of an individual’s propensity to use specific healthcare
services, defined by 25 major diagnostic categories, selected
diagnostic related groups or specific medical service areas by
employing data mining techniques [18]. This index, based on
millions of healthcare transactions of several million patients,
can identify patients who can benefit most from specific
healthcare services, encourage patients who most need specific care to access it, and continually refine the channels and
messages used to reach appropriate audiences for improved
health and long-term patient relationships and loyalty. The
index has been used by OSF Saint Joseph Medical Centre to
IJSER © 2013
http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013
ISSN 2229-5518
get the right messages and services to the most appropriate
patients at strategic times. The end result is more effective and
efficient communications as well as increased revenue [18].
According to Miller, [12] the data mining of patient survey
data can help set reasonable expectations about waiting times,
reveal possible ways to improve service, and provide
knowledge about what patients want from their healthcare
providers. CRM in healthcare can help promote disease education, prevention, and wellness services [19]. Florida Hospital
has used data mining to segment Medicare patients as well as
develop commercial applications that enable credit scoring,
debt collection, and analysis of financial data [8,16]. Sinai
Health System had used of data mining for healthcare marketing and CRM.
Fraud and abuse: Data mining applications that are used to
detect fraud and abuse often establish norms and then identify
unusual or abnormal patterns of claims by physicians, laboratories, clinics, or others. Among other things, these applications can highlight inappropriate prescriptions or referrals and
fraudulent insurance and medical claims. For example, the
Utah Bureau of Medicaid Fraud has mined the mass of data
generated by millions of prescriptions, operations and treatment courses to identify unusual patterns and uncover fraud
[12]. As a result of fraud and abuse detection, ReliaStar Financial Corp. has reported a 20 percent increase in annual savings,
Wisconsin Physician’s Service Insurance Corporation has noted significant savings [13] and the Australian Health Insurance Commission has estimated tens of millions of dollars of
annual savings.
EXAMPLE OF DATA MINING IN MEDICINE
Alzheimer’s disease is a syndrome of gradual onset and
continuing decline of higher cognitive functioning. It is a
common disorder in older persons and becomes more prevalent in each decade of life. Approximately 10% of adults 65
years and older, and 50% of adults older than 90 years, have
dementia. The sample consisted of initial visits of 496 subjects
seen either as control or as patients (Table 1). The Neurologists
and Neurophysiologists apply the DSM-IV criteria along with
some laboratory investigations to classify the subject’s mental
status as either normal, cognitively impaired i.e. dementia of
Alzheimer’s type. The data set consisted of 35 attributes
broadly classified under five main observations namely age,
neuropsychiatry assessments (table 2), mental status examination and laboratory investigations. Entire description of patient’s data set is shown in (Table 1). The objective of the present work was on classification of various stages of Alzheimer’s disease, exploration of various possible treatments
and the management for each stage of AD [2].
917
Observation
Age
Neuropsychiatric
Assessment
Mental Status
Examination
Laboratory
Investigations
Stage of disease
Description
Age in years
Cognitive changes,
Psychiatric symptoms,
Personality change
Problem behaviours,
Family history
MMSE, BIMC, BOMC
STMS and AMT
Blood tests, Complete
blood count, Serum
Chemistry, Thyroid
testing, Apolipoprotein
E testing, Heavy metal
screen for mercury
Human Immunodeficiency Virus
Alzheimer’s disease
Values
Continuous
Yes / No
Continuous
Continuous
Four stages :
Normal/Mild
Moderate,
Severe
Table 1: Description of Patient Data base
NEUROPSYCHOLOGICAL EXAMINATIONS
The primary symptoms of dementia of Alzheimer’s type
are impairments in cognition, behavior, and function, a thorough mental status examination is a necessary part of the
evaluationA number of instruments have been developed for
this purpose. In the present study five important instruments
such as Mini-Mental State Examination, Blessed Information
Memory Concentration, Blessed Orientation Memory Concentration, Short Test of Mental Status and Functional Activity
Questionnaire were employed. These instruments measure the
performance in similar areas of cognitive function and take 5
to 10 minutes to administer and score. Each is reliable for ruling out dementia when results are negative.
LABORATORY INVESTIGATIONS
Various laboratory investigations such as complete blood cell
count, thyroid-stimulating hormone level, serum electrolytes,
serum calcium, and serum glucose to exclude potential infections or metabolic causes for cognitive impairment. Other testing, such as serology for syphilis, human immunodeficiency
virus (HIV), urine analysis, culture and sensitivity, heavy metal assays, liver function, serum folic acid level, were considered in the present study and these tests should be performed
only when clinical suspicion warrants.
ARCHITECTURE AND MODELING
The Architecture and modelling of the present work is depicted in Figure 2. The work begun with the collection of patient’s records, which was followed by pre-processing of data
IJSER © 2013
http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013
ISSN 2229-5518
set, includes converting the data in to numeric values. Next
stage was evaluation of the attributes by using gain ratio attribute evaluation scheme with ranker search method. The
main focus of the work was the classification of various stages
of AD using various Machine learning methods. The last and
the most important stage are treatment and management of
AD, in this stage we suggest different approaches for the
management for each of the classified stages of AD were suggested.
Perceptron
CANFIS
918
99.55
0.009
99.52
99.93
Table 8.6: Results of Classification Accuracies using Various
Machine Learning Methods
PRE-PROCESSING PATIENT’S RECORDS
The collected data was free from outliers, noise and missing data. Some inconsistencies were recorded in the data set
and those consistencies were corrected manually by using external references.
Collection of
Patient’s Records
Classifiers
Treatment
Strategies
C4.5
Preprocessing of
Records
Bagging
Management
of AD
CANFIS
Selection of Attributes
Figure 8.5: Output Vs Desired Plot for the Alzheimer’s
Database
Multilayer
Perceptron
Figure 1: Architecture of Classification of Different Stages of
Alzheimer’s Disease
The data set in the present study consisted of 496 training
cases and 225 testing cases, with 35 attributes broadly classified under five main observations namely age, neuropsychiatry assessments, mental status examination, laboratory investigations and physical examinations (Table 8.5).
Class
Training
data set
Testing
data set
Normal
98
Mild
140
62
68
Moderate
138
Severe
120
50
45
Figure 8.6: Learning Curve for the Alzheimer’s Database
Table 8.5: Description of Data Classification
ML
Methods
C4.5
Bagging
Neural Networks
Multilayer,
ClassificationRuntime Sensitivity Specificity
Accuracy (msec)
(%)
(%)
98.97
0.023
98.61
97.94
98.44
0.025
97.92
98.68
98.25
0.018
98.62
98.89
98.99
0.015
99.01
Class
Normal
Mild
Moderate
Severe
True
62
67
50
45
False
0
0
1
0
Table 8.7: Classification Matrix for Test set (225 test samples)
98.94
IJSER © 2013
http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013
ISSN 2229-5518
Results of classification accuracies using various ML methods are shown in Table 5.6.1. The classification accuracy for
CANFIS was found to be 99.55% which was found to be better
when compared to other classification methods. Classification
accuracy is almost same for C4.5 and multilayer Perceptron,
but the runtime for C4.5 is comparatively higher. The classification matrix of C4.5 obtained for 225 testing sample set is
shown in table 5.6.2. The classification matrix provides a comprehensive picture of the classification performance of the
classifier. The ideal classification matrix is the one in which the
sum of the diagonal is equal to the number of samples. Figure
5.6.1 shows the Output Vs Desired Plot for the test set using
CANFIS and genetic algorithm. Once the classification is done
the next stage would be finding the best possible treatment
and management for Alzheimer’s diseases.
Healthcare data mining can be limited by the accessibility of
data, because the raw inputs for data mining often exist in
different settings and systems, such as administration, clinics,
laboratories and more. Hence, the data have to be collected
and integrated before data mining can be done. Oakley [21]
has suggested a distributed network topology instead of a data warehouse for more efficient data mining, and Friedman
and Pliskin[22] have documented a case study of Maccabi
Healthcare Services using existing databases to guide subsequent data mining. Secondly, other data problems may arise.
These include missing, corrupted, inconsistent, or nonstandardized data, such as pieces of information recorded in
different formats in different data sources. In particular, the
lack of a standard clinical vocabulary is a serious hindrance to
data mining. Cios and Moore [23] have argued that data problems in healthcare are the result of the volume, complexity
and heterogeneity of medical data and their poor mathematical characterization and non-canonical form. Further, there
may be ethical, legal and social issues, such as data ownership
and privacy issues, related to healthcare data. The quality of
data mining results and applications depends on the quality of
data.32 Thirdly, a sufficiently exhaustive mining of data will
certainly yield patterns of some kind that are a product of
random fluctuations [24]. This is especially true for large data
sets with many variables. Hence, many interesting or significant patterns and relationships found in data mining may not
be useful. Hand [25] and Murray [26] have warned against
using data mining for data dredging or fishing, which is randomly trawling through data in the hope of identifying patterns. Fourthly, the successful application of data mining requires knowledge of the domain area as well as in data mining
methodology and tools. Without a sufficient knowledge of
data mining, the user may not be aware of or be able to avoid
the pitfalls of data mining [27]. Collectively, the data mining
919
team should possess domain knowledge, statistical and research expertise, and IT and data mining knowledge and
skills.
DISCUSSION
The healthcare data are not limited to just quantitative data,
such as physicians’ notes or clinical records, it is necessary to
also explore the use of text mining to expand the scope and
nature of what healthcare data mining can currently do. In
particular, it is useful to be able to integrate data and text mining [28]. It is also useful to look into how digital diagnostic
images can be brought into healthcare data mining applications. Some progress has been made in these areas [29.30].
Pharmaceutical companies can benefit from healthcare CRM
and data mining, too. By tracking which physicians prescribe
which drugs and for what purposes, pharmaceutical companies can decide whom to target, show what is the least expensive or most effective treatment plan for an ailment, help identify physicians whose practices are suited to specific clinical
trials (for example, physicians who treat a large number of a
specific group of patients), and map the course of an epidemic
to support pharmaceutical salespersons, physicians, and patients [31]. Pharmaceutical companies can also employ data
mining methods to huge masses of genomic data to predict
how a patient’s genetic makeup determines his or her response
to a drug therapy [32].
The identification and quantification of pharmaceutical information can be extremely useful for patients, physicians,
pharmacists, health organizations, insurance companies, regulatory agencies, investors, lawyers, pharmaceutical manufacturers, drug testing companies, etc. For example information
on interactions among over-the-counter medicines, interactions between prescription and over-the-counter medicines,
interactions among prescription medicines, interactions between any kind of medicine and various foods, beverages,
vitamins, and mineral supplements, common characteristics
between certain drug groups and offending foods, beverages,
medicines, will yield very useful results using data mining
techniques [33]. One of the major problems with pharmaceutical data is actually a lack of information. Most health care providers simply do not have the time to fill out reports of possible adverse drug reactions. Furthermore, it is expensive and
time-consuming for pharmaceutical companies to perform a
thorough job of data collection, especially when most of the
information is not required by law [34].
The delivery of health care has always been information intensive, and there are signs that the industry is recognizing the
increasing importance of information processing in the new
managed care environment [35]. Most healthcare institutions
IJSER © 2013
http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013
ISSN 2229-5518
lack the appropriate information systems to produce reliable
reports with respect to other information than purely financial
and volume related statements [36] stress that. The management of pharma industry starts to recognize the relevance of
the definition of drugs and products in relation to management of critical information. In the turmoil between costs,
care-results and patient satisfaction the right balance is needed
and can be found in upcoming information and communication technology. Research shows that successful decision systems enriched with analytical solutions are necessary for
healthcare information systems [37-39].
A user-interface may be designed to accept all kinds of information from the user (e.g., weight, sex, age, foods consumed, reactions reported, dosage, length of usage). Then,
based on the information in the databases and the relevant
data entered by the user, a list of warnings or known reactions
(accompanied by probabilities) should be reported. Note that
user profiles can contain large amounts of information, and
efficient and effective data mining tools need to be developed
to probe the databases for relevant information. Second, the
patient’s (anonymous) profile should be recordedalong with
any adverse reactions reported by the patient, so that future
correlations can be reported. Over time, the databases will
become much larger, and interaction data for existing medicines will become more complete [34].
Data mining can provide intelligence in terms of: drug positioning information, patent population characteristics, indications of what the drug is being used for, prescribing physician
characteristics, regional preferences, prevalence of diseases,
preferred drug for diseases, procedures being performed,
disease-related information.
APPLICATIONS OF DATA MINING IN THE PHARMACEUTICAL INDUSTRY
 Clinical data analysis – clinical data analysis evaluates
and streamlines from large amount of information.
Data mining helps to see trends, irregularity, and risk
during product development and launch.
 Marketing and sales analysis –the identification of the
most profitable product and allocation of marketing
funds. Data mining here helps to examine consumer
behavior in terms of prescription renewal and product purchases.
 Customer analysis – using data mining one can develop more targeted customer profiles that focus not
only on products, but also on the ability to pay for
them by analyzing historical health trends in combination with demographics.
 Identify and target individuals and demographics
that could be considered “undiagnosed” with educa-









920
tional campaigns whose goal is to encourage these
individuals to get screened and tested for possible issues.
Combine product sales information with customer
groups and customer channel information to analyze
what tends to lead customers to fill prescriptions at a
more consistent rate or what leads physicians to prescribe certain drugs at a higher rate.
Operations and financial analysis – analyze the prescription activity in a geographic region or area to
make sales force adjustments according to market size
or penetration.
Dissect buying trends from the largest customers
(managed care providers and governments) to proactively create price points that benefit both the buyer
and the organization.
Sales and marketing analysis – provide mobile analytics to a sales force that is consistently disconnected,
allowing them to answer not only detailed drug information questions, but also historical and trending
questions.
Target physicians who have high prescription rates of
a certain drug or treatment with new drug information that treat complementary symptoms or conditions.
Product analysis – analyze buying tendencies and
treatment outcomes to create more drug and product
variations tailored directly towards different age
groups and risk factors.
Combine demographics and patient historical trends
to target “quality of life” needs of patients (i.e. lifestyle drugs) that improve the day-to-day living
standards of patients, especially for non-acute medical conditions.
Supply chain analysis – improve production schedules through analysis of which pharma products stay
on the shelves the longest and how well each pharma
product is selling.
Manage inventories more efficiently based on historical trends and patient behavior to prevent stock-outs
at retail and pharmacy locations [34].
DATA MINING IN DISCOVERY OF NEW DRUGS
Here the data mining techniques used are clustering, classification and neural networks. The goal is to determine compounds with similar activity. The reason for this is the compounds with similar activity may behave similarly. This
should be performed when we know the compound and are
looking for something better or when we do not know the
compound, but have desired activity and want to find compound that exhibits similar activity. This can be achieved by
clustering the molecules into groups according to the chemical
IJSER © 2013
http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013
ISSN 2229-5518
properties of the molecules via cluster analysis [40]. This way
every time a new molecule is discovered it can be grouped
with other chemically identical molecules. This would help the
researchers in finding out with therapeutic group the new
molecule would belong to. Mining can help us to measure the
chemical activity of the molecule on specific disease say tuberculosis and find out which part of the molecule is causing the
action. This way we can combine a vast number of molecules
forming a super molecule with only the specific part of the
molecule that is responsible for the action and inhibiting the
other parts. This would greatly reduce the adverse effects associated with drug actions.
DATA MINING IN CLINICAL TRIALS
Pharmaceutical companies test drugs in patients on larger
scale. The company has to keep track of data about patient
progress. The data obtained undergoes statistical analysis to
determine the success of a trial. Data are generally reported to
a food and drug administration department and inspected
closely. Too many adverse reactions might indicate a drug is
too dangerous. Adverse events are reported to the food and
drug administration when a link is suspected. The effectiveness of the drug is often measured by how soon the drug deals
with the medical condition [41]. A simple association technique could help to measure the outcomes that would greatly
enhance the patient’s quality of life such as faster restoration of
the body’s normal functioning.
A data mining contest [42] was conducted held to predict the
molecular bioactivity for a drug design; specifically, determining which organic molecules would bind to a target site on
thrombin. The predictions were based on about 500 megabytes of data on approximately 1,900 organic molecules, each
with more than 130,000 attributes (or dimensions, as they are
called in data mining). This was a challenging problem not
only because of the large number of attributes but because
only 42 of the compounds (2.2%) were active. The relatively
small number of cases (organic molecules in this example),
compared to the large number of attributes, makes the problem even more difficult. Of the 136 contest entries, about 10%
achieved the impressive result of more than 60% accuracy [43],
with the winner, Jie Cheng of the Canadian Imperial Bank of
Commerce, reaching almost 70% accuracy.
DATA MINING AND THE FUTURE OF PHARMACEUTICAL INDUSTRY
New approaches are needed in order to fully realize the potential of technologies that allow for the creation, acquisition,
storage and analysis of databases of unprecedented size and
complexity. The modern company needs to view itself as a
data machine whose primary business is collecting, processing, analysing and using its data as the primary resource
921
of the company. This trend will continue and accelerate. Genomic and related technologies allow for more data to be collected on each patient both during the development of a drug
and after it is marketed. General IT technologies continue to
move in a direction that allows more data to be collected, processed and analysed. There are demands for more information
about each product from patients, physicians and regulators.
Appropriate data, the structure, job descriptions and likely
organizations will change. We are living in an age that emphasizes a great increase in collection and use of data. It is not
surprising that the pharmaceutical industry, which has been
an information industry for decades, is even more affected by
these trends. The fact that some of these changes have happened so quickly and that many in pharmaceutical and medical research did not recognize the previous emphasis on information may have disguised the cataclysmic events taking
place. Hence, data mining will play a major and pivotal role in
pharmaceutical and health care industry in future.
REFERENCES
[1]
Sandhya Joshi, P. Deepa Shenoy, Venugopal K R, L M Patnaik
Data Analysis and Classification of Various Stages of Dementia
Adopting Rough Sets Theory. International Journal on Information
processing,4(1), 86-99, 2010.
[2] Sandhya Joshi, P. Deepa Shenoy, Venugopal K R, L M Patnaik A.
Classification and treatment of different stages of Alzheimer’s
disease using various machine learning methods International
Journal of Bioinformatics Research, ISSN: 0975–3087, Volume 2,
Issue 1, 2010, pp-44-52.
[3] Sandhya Joshi, P. Deepa Shenoy, Venugopal K R, L M Patnaik
“Classification of Neurodegenerative Disorders Based on Major Risk Factors Employing Machine Learning Techniques” International Journal of Engineering and Technology. ISSN: 1793-8236,
Vol.2, No.4, August 2010. pp. 350-355.
[4] Sandhya Joshi, P. Deepa Shenoy, Venugopal K R, L M Patnaik
Statistical Classification of Magnetic Resonance Images of Brain
Employing Random Forest Classifier. International Journal of Machine Intelligence, ISSN: 0975–2927, vol 1, Issue 2, 2009, pp.55-61.
[5] Silver, M. Sakata, T. Su, H.C. Herman, C. Dolins, S.B. & O’Shea,
M.J. (2001). Case study: how to apply data mining techniques in
a healthcare data warehouse. Journal of Healthcare Information
Management, 15(2), 155-164.
[6] Benko, A. & Wilson, B. (2003). Online decision support gives
plans an edge. Managed Healthcare Executive, 13(5), 20.
[7] Gillespie, G. (2000). There’s gold in them thar’ databases. Health
Data Management, 8(11), 40-52.
[8] Kolar, H.R. (2001). Caring for healthcare. Health Management
Technology, 22(4), 46-47.
[9] Relles, D. Ridgeway, G. & Carter, G. (2002). Data mining and the
implementation of a prospective payment system for inpatient
rehabilitation. Health Services & Outcomes Research Methodology,
3(3-4), 247-266.
[10] Robert D., Small and Herbert A. Edelstein. Data mining in the
pharmaceutical industry. Drug Discovery World Fall 2001.
[11] Kincade, K. (1998). Data mining: digging for healthcare gold. Insurance & Technology, 23(2), IM2-IM7.
[12] Milley, A. (2000). Healthcare and data mining. Health Management Technology, 21(8), 44-47.
IJSER © 2013
http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013
ISSN 2229-5518
[13] Young, J. & Pitta, J. (1997). Wal-Mart or Western Union? United
HealthCare Corp. Forbes, 160(1), 244.
[14] Veletsos, A. (2003). Getting to the bottom of hospital finances.
Health Management Technology, 24(8), 30-31.
[15] Dakins, D.R. (2001). Center takes data tracking to heart. Health
Data Management, 9(1), 32-36.
[16] Johnson, D.E.L. (2001). Web-based data analysis tools help providers, MCOs contain costs. Health Care Strategic Management,
19(4), 16-19.
[17] Schuerenberg, B.K. (2003). An information excavation. Health
Data Management, 11(6), 80-82.
[18] Paddison, N. (2000). Index predicts individual service use.
Health Management Technology, 21(2), 14-17.
[19] Hallick, J.N. (2001), Analytics and the data warehouse. Health
Management Technology, 22(6), 24-25.
[20] Anonymous. (1999). Texas Medicaid Fraud and Abuse Detection
System recovers $2.2 million, wins national award. Health Management Technology, 20(10), 8.
[21] Oakley, S. (1999). Data mining, distributed networks and the laboratory. Health Management Technology, 20(5), 26-31.
[22] Friedman, N.L. & Pliskin, N. (2002). Demonstrating valueadded utilization of existing databases for organizational decision-support. Information Resources Management Journal, 15(4), 115.
[23] Cios, K.J. & Moore, G.W. (2002). Uniqueness of medical data
mining. Artificial Intelligence in Medicine, 26(1), 1-24.
[24] Chopoorian, J.A. Witherell, R. Khalil, O.E.M. & Ahmed, M.
(2001). Mind your own business by mining your data. SAM Advanced Management Journal, 66(2), 45-51.
[25] Hand, D.J. (1998). Data mining: statistics and more? The American Statistician, 52(2), 112-118.
[26] Murray, L.R. (1997). Lies, damned lies and more statistics: the
neglected issue of multiplicity in accounting research. Accounting and Business Research, 27(3), 243-258.
[27] McQueen, G. & Thorley, S. (1999). Mining fool’s gold. Financial
Analysts Journal, 55(2), 61-72.
[28] Cody, W.F. Kreulen, J.T. Krishna, V. & Spangler, W.S. (2002). The
integration of business intelligence and knowledge management. IBM Systems Journal, 41(4), 697-713.
[29] Ceusters, W. (2001). Medical natural language understanding as
a supporting technology for data mining in healthcare. In Medical Data Mining and Knowledge Discovery, Cios, K. J. (Ed.), Physica- Verlag Heidelberg, New York, 41-69.
[30] Megalooikonomou, V. & Herskovits, E.H. (2001). Mining structurefunction associations in a brain image database. In Medical
Data Mining and Knowledge Discovery, Cios, K. J. (Ed.), PhysicaVerlag Heidelberg, New York, 153-180.
[31] Brannigan, M. (1999). Quintiles seeks mother lode in health “data mining.” Wall Street Journal, March 2, 1.
[32] Thompson, W. & Roberson, M. (2000). Making predictive medicine
possible. R & D, 42(6), E4-E6.
[33] Hian Chye Koh and Gerald Tan. Data Mining Applications in
Healthcare. Journal of Healthcare Information Management —
Vol. 19, No. 2, 5-11.
[34] Jayanthi Ranjan Data mining in pharma sector: Benefits , International Journal of health care quality assurance, 22(1), 2009, pp.
82-92.
[35] Morrisey, J. (1995), “Managed care steers info systems”, Modern
Healthcare, Vol. 25 No. 8.
[36] Prins, S. and Stegwee, R.A. (2000), “Zorgproducten en geintegreerde infromatie systemen”, Handboek sturen met
[37]
[38]
[39]
[40]
[41]
[42]
[43]
IJSER © 2013
http://www.ijser.org
922
zorgproducten, F3100-3, Bohn Stafleu Van Loghum, Houten,
December (in Dutch).
Zuckerman, A.M. (2006), Healthcare Strategic Planning, Prentice-Hall of India, New Delhi.
Armoni, A. (2002), Effective Healthcare Information Systems,
IRM Press, Hershey, PA.
Rada, R. (2002), Information Systems for Healthcare Enterprises,
Hypermedia Solutions,
a.
Liverpool.
De Cooman, F. (2005), Data Mining in a Pharmaceutical Environment, Belgium Press, Brussels.
Graedon, J. and Graedon, T. (1996), People’s Guide to Deadly
Drug Interactions, St Martin’s Press, New York, NY.
Knowledge Discovery in Databases: 10 Years After. SIGKDD
Explorations, ACM SIGKDD V.1 59-61, 2000.
Edelstein, Herb. Data Mining Technology Report: 2002, Forthcoming, Two Crows Corporation.